Journal
ACS SYNTHETIC BIOLOGY
Volume 8, Issue 10, Pages 2385-2396Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acssynbio.9b00255
Keywords
biosensor; transcription factor; Pseudomonas putida; glutarate; Monte Carlo Markov chain
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Funding
- UC Berkeley SMART program
- U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research
- U.S. Department of Energy, Energy Efficiency and Renewable Energy, Bioenergy Technologies Office [DE-AC02-05CH11231]
- Basque Government through the BERG 2018-2021 program
- Spanish Ministry of Economy and Competitiveness MINECO: BCAM Severo Ochoa excellence accreditation [SEV-2017-0718]
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A significant bottleneck in synthetic biology involves screening large genetically encoded libraries for desirable phenotypes such as chemical production. However, transcription factor-based biosensors can be leveraged to screen thousands of genetic designs for optimal chemical production in engineered microbes. In this study we characterize two glutarate sensing transcription factors (CsiR and GcdR) from Pseudomonas putida. The genomic contexts of csiR homologues were analyzed, and their DNA binding sites were bioinformatically predicted. Both CsiR and GcdR were purified and shown to bind upstream of their coding sequencing in vitro. CsiR was shown to dissociate from DNA in vitro when exogenous glutarate was added, confirming that it acts as a genetic repressor. Both transcription factors and cognate promoters were then cloned into broad host range vectors to create two glutarate biosensors. Their respective sensing performance features were characterized, and more sensitive derivatives of the GcdR biosensor were created by manipulating the expression of the transcription factor. Sensor vectors were then reintroduced into P. putida and evaluated for their ability to respond to glutarate and various lysine metabolites. Additionally, we developed a novel mathematical approach to describe the usable range of detection for genetically encoded biosensors, which may be broadly useful in future efforts to better characterize biosensor performance.
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